The most niche AI technology won’t make an impact if external audiences can’t understand it.
Bospar account supervisor AJ Jean would know. He helps B2B technology companies turn complex ideas into stories that resonate with media.
In this Expert Insights interview, AJ shares how to speak an audience’s language and make even the most technical concepts impossible for media to ignore.
What’s the biggest mistake companies make when trying to explain technical ideas/complicated themes to external audiences?
Not adapting to the level of the audience they want to reach. Companies forget that not all readers are as involved in their industries. Some are more technical, others less so. It helps when companies simplify their value proposition and purpose as much as possible.
Similarly, it’s valuable to know when using data makes sense. Not everyone can quantify outcomes, so doing it in a way that makes direct comparisons or establishes a benchmark (e.g., “My product performs 75% better than the competition”) is important.
For example, we had an AI client share benchmarking data pitting them against other AI companies, and it was vital in messaging their strength to reporters and their audiences.
What’s your process for understanding and translating a highly technical or abstract concept for broader audiences?
Besides reading and research, materials like tech specs and fundraising decks are fundamental to fully understanding what the client’s key challenges and value props are surrounding a technical subject.
I also like to ask clients, “How would you explain this to someone who hasn’t heard about your company?” PR can’t explain a concept to reporters if we don’t understand it ourselves.
When translating dense topics like AI, it helps to analogize challenges into everyday concepts. Many people understand the issue of energy and water consumption for AI tools. Making comparisons on how much or little energy a client’s company saves provides a real example.
Similarly, avoiding jargon is key. While jargon may make sense to internal stakeholders, it only obfuscates messages for more green audiences, when the point is to reach those audiences.
How do you determine what information is essential to keep versus what can be left out?
This comes down to understanding the audience. Keeping messaging at a high level and using real-world examples makes the most sense if they’re not well-versed in the client’s industry.
When a more technical outlet like The Information covered the differences in AI architecture, we were able to emphasize a client’s neuromorphic architecture and how it differs from more common transformer-based models.
For wider outlets looking to cover mainstream stories like the growth of data centers, the PR team shifted our focus to practical impact and highlighted the client’s efficiency advantages, like less energy consumption. It’s about tailoring a message for different readers and purposes.
What questions should companies ask themselves before communicating a complicated idea to media?
How involved is the reporter and their audience in the sector? Does the reporter write on this topic often? Do we have diagrams or data that can simplify messaging?
That context and collateral are helpful when presenting complex themes and ideas to media and, ultimately, determine if they’ll move forward with a story.
What’s a complex topic that initially seemed difficult to communicate, but ultimately became much more powerful once it was simplified?
We pitched a client’s perspective on neuromorphic AI. For most reporters, the idea of neuromorphic AI is hard to digest. Many people don’t understand how the brain works, much less how an AI system does.
PR’s approach was to extract how the architecture is beneficial (it runs on less hardware) and how much less energy it uses (nearly 10x less than industry-standard GPU-reliant programs).
That way, the reporter was able to understand the outcomes much easier.
What coverage are you most proud of that came from your ability to break down its complicated themes to media?
We secured a feature piece for a client in The Next Platform about how they’re applying neuromorphic computing principles to their two AI platforms. Throughout the article, the CEO used examples the reader could relate to to better understand the complex concept, like comparing model pruning to sleep. That’s an example that resonates with most audiences and drives the point home.
